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:: دوره 10، شماره 3 - ( 11-1401 ) ::
جلد 10 شماره 3 صفحات 70-49 برگشت به فهرست نسخه ها
ارائه یک روش کارآمد با استفاده از ادغام ویژگی ھای شبکه عصبی کانولوشنی عمیق برای تشخیص ابر به کمک باندھای بازتابی از تصاویر ماھواره ای لندست-8
ارسطو زارعی ، رضا شاه حسینی* ، مرتضی سیدموسوی
دانشگاه تهران
چکیده:   (1553 مشاهده)
شناسایی ابر در کاربردهای مختلف تصاویر ماهواره­ای چندطیفی مرحله­ای مهم در پیش پردازش می­باشد، به طور خاص در برنامه­های مرتبط با بلایای طبیعی مانند نظارت بر سیل یا نقشه برداری سریع خسارت که در بحث زمان و داده­ها دارای اولویت هستند و نیاز به روش­هایی دارند که ماسک ابری دقیق را در مدت زمان کوتاه به طور آنی تولید کنند. در این مطالعه، یک شبکه عصبی پیچیده عمیق برای تشخیص ابر در مجموعه داده­های لندست-8 در سطح پیکسل ارائه شد. شبکه پیشنهادی در این مطالعه دو ویژگی اصلی داشت: 1) چندین هسته پیچشی با اندازه‌های چندگانه، و 2) لایه­های کانولوشنی مستقیم در شاخه رمزگشا. باند مادون قرمز نزدیک در این مطالعه به ورودی­های شبکه شامل باندهای قرمز، سبز و آبی اضافه شد تا عملکرد شبکه را بهبود ببخشد. در معماری شبکه پیشنهادی، شاخه­های رمزگذار-رمزگشای متقارن با تراکم نقشه­های ویژگی حاصل از تعدد فیلترها و طراحی فیلترهای با ابعاد مختلف، زمینه محلی و کلی را جهت شناسایی دقیق ابر و حاشیه­های آن فراهم کردند که برای استخراج ویژگی­های مکانی در مقیاس­های سطح بالا استفاده می­شوند. نقشه­های ویژگی حاصل از مقیاس­های متعدد، نمونه­برداری و تلفیق شده و جهت بازیابی خروجی با دقت­های بالا به کار گرفته می­شوند. در نهایت روش پیشنهادی با استفاده از 3500 قطعه از تصاویر ماهواره لندست-8 با چالش­های متنوع ابر با به کارگیری از چندین هسته در اندازه­های 3 × 3 و 5 × 5 با نمره F1 برابر 6/96 و شاخص ژاکارد 5/93 نسبت به روش­های دیگر دقت بالاتری را ارائه داد. به طور کلی در روش پیشنهادی نسبت به روش­های مقایسه شده در مجموعه داده یکسان اما تصحیح نشده، به ویژه در مناطق پوشیده از سطح روشن، نتایج بهتری را به دست آورد.
واژه‌های کلیدی: سنجش از دور، لندست-8، شبکه عصبی کانولوشنی، شناسایی ابر.
متن کامل [PDF 1461 kb]   (386 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: سنجش از دور
دریافت: 1400/4/30 | پذیرش: 1401/9/1 | انتشار: 1401/11/17
فهرست منابع
1. [1] Zhu, Zhe, Shixiong Wang, and Curtis E. Woodcock. "Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images." Remote sensing of Environment 159 (2015): 269-277.
2. [2] Braaten, Justin D., Warren B. Cohen, and Zhiqiang Yang. "Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems." Remote Sensing of Environment 169 (2015): 128-138.
3. [3] Lin, Chao-Hung, Bo-Yi Lin, Kuan-Yi Lee, and Yi-Chen Chen. "Radiometric normalization and cloud detection of optical satellite images using invariant pixels." ISPRS Journal of Photogrammetry and Remote Sensing 106 (2015): 107-117.
4. [4] Wu, Teng, Xiangyun Hu, Yong Zhang, Lulin Zhang, Pengjie Tao, and Luping Lu. "Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction." ISPRS Journal of Photogrammetry and Remote Sensing 121 (2016): 143-156.
5. [5] Choi, Hyeungu, and Robert Bindschadler. "Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision." Remote Sensing of Environment 91, no. 2 (2004): 237-242.
6. [6] Irish, Richard R., John L. Barker, Samuel N. Goward, and Terry Arvidson. "Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm." Photogrammetric engineering & remote sensing 72, no. 10 (2006): 1179-1188.
7. [7] Zhu, Xiaolin, and Eileen H. Helmer. "An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions." Remote sensing of environment 214 (2018): 135-153.
8. [8] Zhu, Zhe, and Curtis E. Woodcock. "Object-based cloud and cloud shadow detection in Landsat imagery." Remote sensing of environment 118 (2012): 83-94.
9. [9] Ishida, Haruma, Yu Oishi, Keitaro Morita, Keigo Moriwaki, and Takashi Y. Nakajima. "Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions." Remote sensing of environment 205 (2018): 390-407.
10. [10] Frantz, David, Erik Haß, Andreas Uhl, Johannes Stoffels, and Joachim Hill. "Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects." Remote sensing of environment 215 (2018): 471-481.
11. [11] Mei, Linlu, Marco Vountas, Luis Gómez-Chova, Vladimir Rozanov, Malte Jäger, Wolfhardt Lotz, John P. Burrows, and Rainer Hollmann. "A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data." Remote Sensing of Environment 197 (2017): 141-160.
12. [12] Hollingsworth, Ben V., Liqiang Chen, Stephen E. Reichenbach, and Richard R. Irish. "Automated cloud cover assessment for Landsat TM images." In Imaging Spectrometry II, vol. 2819, pp. 170-179. SPIE, 1996.
13. [13] Scaramuzza, Pasquale L., Michelle A. Bouchard, and John L. Dwyer. "Development of the Landsat data continuity mission cloud-cover assessment algorithms." IEEE Transactions on Geoscience and Remote Sensing 50, no. 4 (2011): 1140-1154.
14. [14] Vermote, Eric, Chris Justice, Martin Claverie, and Belen Franch. "Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product." Remote Sensing of Environment 185 (2016): 46-56.
15. [15] Wang, Bin, Atsuo Ono, Kanako Muramatsu, and Noboru Fujiwara. "Automated detection and removal of clouds and their shadows from Landsat TM images." IEICE Transactions on information and systems 82, no. 2 (1999): 453-460.
16. [16] Jin, Suming, Collin Homer, Limin Yang, George Xian, Joyce Fry, Patrick Danielson, and Philip A. Townsend. "Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA." International Journal of Remote Sensing 34, no. 5 (2013): 1540-1560.
17. [17] Molnar, G., and J. A. Coakley Jr. "Retrieval of cloud cover from satellite imagery data: A statistical approach." Journal of Geophysical Research: Atmospheres 90, no. D7 (1985): 12960-12970.
18. [18] Ricciardelli, E., F. Romano, and V. Cuomo. "Physical and statistical approaches for cloud identification using meteosat second generation-spinning enhanced visible and infrared imager data." Remote sensing of environment 112, no. 6 (2008): 2741-2760.
19. [19] Lee, Yoonkyung, Grace Wahba, and Steven A. Ackerman. "Cloud classification of satellite radiance data by multicategory support vector machines." Journal of Atmospheric and Oceanic Technology 21, no. 2 (2004): 159-169. https://doi.org/10.1175/1520-0426(2004)021<0159:CCOSRD>2.0.CO;2 [DOI:10.1175/1520-0426(2004)0212.0.CO;2]
20. [20] Tian, Bin, Mukhtiar A. Shaikh, Mahmood R. Azimi-Sadjadi, Thomas H. Vonder Haar, and Donald L. Reinke. "A study of cloud classification with neural networks using spectral and textural features." IEEE transactions on neural networks 10, no. 1 (1999): 138-151.
21. [21] Chai, Dengfeng, Shawn Newsam, Hankui K. Zhang, Yifan Qiu, and Jingfeng Huang. "Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks." Remote sensing of environment 225 (2019): 307-316.
22. [22] Yu, Xiaohe, and David J. Lary. "Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification." Remote Sensing 13, no. 16 (2021): 3289.
23. [23] Qiu, Shi, Zhe Zhu, and Binbin He. "Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery." Remote Sensing of Environment 231 (2019): 111205.
24. [24] He, Xing Yuan, Jian Bo Hu, Wei Chen, and Xiao Yu Li. "Haze removal based on advanced haze-optimized transformation (AHOT) for multispectral imagery." International Journal of Remote Sensing 31, no. 20 (2010): 5331-5348.
25. [25] Mohajerani, Sorour, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, and Parvaneh Saeedi. "Cloudmaskgan: A content-aware unpaired image-to-image translation algorithm for remote sensing imagery." In 2019 IEEE International Conference on Image Processing (ICIP), pp. 1965-1969. IEEE, 2019.
26. [26] Aghdami-Nia, Mohammad, Reza Shah-Hosseini, Amirhossein Rostami, and Saeid Homayouni. "Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net." International Journal of Applied Earth Observation and Geoinformation 109 (2022): 102785.
27. [27] Rostami, Amirhossein, Reza Shah-Hosseini, Shabnam Asgari, Arastou Zarei, Mohammad Aghdami-Nia, and Saeid Homayouni. "Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning." Remote Sensing 14, no. 4 (2022): 992.
28. [28] Seyed Mousavi, Seyed Morteza, and Mehdi Akhoondzadeh Hanzaei. "Monitoring and Prediction of the changes in water zone of wetlands using an intelligent neural-fuzzy system based on data from Google Eearth Engine system (Case study of Anzali Wetland, 2000-2019)." Engineering Journal of Geospatial Information Technology 9, no. 4 (2022): 19-42.
29. [29] Zhang, Liangpei, Lefei Zhang, and Bo Du. "Deep learning for remote sensing data: A technical tutorial on the state of the art." IEEE Geoscience and remote sensing magazine 4, no. 2 (2016): 22-40.
30. [30] Zhang, Liangpei, Lefei Zhang, and Bo Du. "Deep learning for remote sensing data: A technical tutorial on the state of the art." IEEE Geoscience and remote sensing magazine 4, no. 2 (2016): 22-40.
31. [31] Hu, Kai, Dongsheng Zhang, Min Xia, Ming Qian, and Binyu Chen. "LCDNet: Light-weighted Cloud Detection Network for High-resolution Remote Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022).
32. [32] LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
33. [33] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
34. [34] Girshick, Ross, Jeff Donahue, Trevor Darrell, and Jitendra Malik. "Rich feature hierarchies for accurate object detection and semantic segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587. 2014.
35. [35] Chen, Liang-Chieh, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs." IEEE transactions on pattern analysis and machine intelligence 40, no. 4 (2017): 834-848.
36. [36] Li, Zhiwei, Huanfeng Shen, Yancong Wei, Qing Cheng, and Qiangqiang Yuan. "Cloud detection by fusing multi-scale convolutional features." ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci 4 (2018): 149-152.
37. [37] Mateo-García, Gonzalo, Luis Gómez-Chova, Julia Amorós-López, Jordi Muñoz-Marí, and Gustau Camps-Valls. "Multitemporal cloud masking in the Google Earth Engine." Remote Sensing 10, no. 7 (2018): 1079.
38. [38] Xie, Fengying, Mengyun Shi, Zhenwei Shi, Jihao Yin, and Danpei Zhao. "Multilevel cloud detection in remote sensing images based on deep learning." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 8 (2017): 3631-3640.
39. [39] Mohajerani, Sorour, and Parvaneh Saeedi. "Cloud-Net: An end-to-end cloud detection algorithm for Landsat 8 imagery." In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 1029-1032. IEEE, 2019.
40. [40] Mohajerani, Sorour, Thomas A. Krammer, and Parvaneh Saeedi. "Cloud detection algorithm for remote sensing images using fully convolutional neural networks." arXiv preprint arXiv:1810.05782 (2018).
41. [41] Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440. 2015.
42. [42] Guo, Jianhua, Jingyu Yang, Huanjing Yue, Hai Tan, Chunping Hou, and Kun Li. "CDnetV2: CNN-based cloud detection for remote sensing imagery with cloud-snow coexistence." IEEE Transactions on Geoscience and Remote Sensing 59, no. 1 (2020): 700-713.
43. [43] Wieland, Marc, Yu Li, and Sandro Martinis. "Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network." Remote Sensing of Environment 230 (2019): 111203.
44. [44] Francis, Alistair, Panagiotis Sidiropoulos, and Jan-Peter Muller. "CloudFCN: Accurate and robust cloud detection for satellite imagery with deep learning." Remote Sensing 11, no. 19 (2019): 2312.
45. [45] Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of training deep feedforward neural networks." In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249-256. JMLR Workshop and Conference Proceedings, 2010.
46. [46] Leshno, Moshe, Vladimir Ya Lin, Allan Pinkus, and Shimon Schocken. "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function." Neural networks 6, no. 6 (1993): 861-867.
47. [47] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Communications of the ACM 60, no. 6 (2017): 84-90.
48. [48] Agarap, Abien Fred. "Deep learning using rectified linear units (relu)." arXiv preprint arXiv:1803.08375 (2018).
49. [49] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." In International conference on machine learning, pp. 448-456. PMLR, 2015.
50. [50] Albawi, Saad, Tareq Abed Mohammed, and Saad Al-Zawi. "Understanding of a convolutional neural network." In 2017 international conference on engineering and technology (ICET), pp. 1-6. Ieee, 2017.
51. [51] Yu, Dingjun, Hanli Wang, Peiqiu Chen, and Zhihua Wei. "Mixed pooling for convolutional neural networks." In International conference on rough sets and knowledge technology, pp. 364-375. Springer, Cham, 2014.
52. [52] Nowlan, Steven, and Geoffrey E. Hinton. "Adaptive soft weight tying using gaussian mixtures." Advances in Neural Information Processing Systems 4 (1991).
53. [53] Srivastava, Nitish, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. "Dropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research 15, no. 1 (2014): 1929-1958.
54. [54] Khoshboresh-Masouleh, Mehdi, and Reza Shah-Hosseini. "A multi-scale convolutional neural network for automatic cloud and cloud shadow detection from Gaofen-1 images." Engineering Journal of Geospatial Information Technology 8, no. 4 (2021): 45-68.
55. [55] Ho, Yaoshiang, and Samuel Wookey. "The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling." IEEE Access 8 (2019): 4806-4813.
56. [56] Ball, John E., Derek T. Anderson, and Chee Seng Chan Sr. "Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community." Journal of applied remote sensing 11, no. 4 (2017): 042609.
57. [57] Moradi, Fatemeh, Farzaneh Dadrass Javan, and Farhad Samadzadegan. "Potential evaluation of visible-thermal UAV image fusion for individual tree detection based on convolutional neural network." International Journal of Applied Earth Observation and Geoinformation 113 (2022): 103011.
58. [58] Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. "Learning deconvolution network for semantic segmentation." In Proceedings of the IEEE international conference on computer vision, pp. 1520-1528. 2015.
59. [59] Chicco, Davide, and Giuseppe Jurman. "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation." BMC genomics 21, no. 1 (2020): 1-13.
60. [60] Gonzales, Cindy, and Wesam Sakla. "Semantic segmentation of clouds in satellite imagery using deep pre-trained U-nets." In 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-7. IEEE, 2019.
61. [61] Mohajerani, Sorour, Thomas A. Krammer, and Parvaneh Saeedi. "Cloud detection algorithm for remote sensing images using fully convolutional neural networks." arXiv preprint arXiv:1810.05782 (2018).
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Zarei A, Shah-Hosseini R, Seyyed-Mousavi M. An efficient method using the fusion of deep convolutional neural network features for cloud detection using Landsat-8 OLI spectral bands. jgit 2023; 10 (3) :49-70
URL: http://jgit.kntu.ac.ir/article-1-839-fa.html

زارعی ارسطو، شاه حسینی رضا، سیدموسوی مرتضی. ارائه یک روش کارآمد با استفاده از ادغام ویژگی ھای شبکه عصبی کانولوشنی عمیق برای تشخیص ابر به کمک باندھای بازتابی از تصاویر ماھواره ای لندست-8. مهندسی فناوری اطلاعات مکانی. 1401; 10 (3) :49-70

URL: http://jgit.kntu.ac.ir/article-1-839-fa.html



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دوره 10، شماره 3 - ( 11-1401 ) برگشت به فهرست نسخه ها
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